The Elevator Safety Monitoring Technology Research And Implementation

In recent years,the number of special equipment such as elevators has been increasing,and the growth rate has been increasing year by year.With such a huge number of elevators,elevator safety accidents occur frequently,so research on elevator safety monitoring has become more and more urgent and necessary.Over the years,machine learning has been widely used in various fields and has produced many high-quality results.Machine learning technology also has considerable application prospects in elevator safety monitoring.The current elevator safety monitoring technology and implementation method can only be used in some elevator equipment,and it is generally expensive and difficult to promote.In this paper,the acceleration signal and air pressure altitude signal when the elevator equipment is running are used as the cut-in angle to realize the safety monitoring of the elevator equipment.The main contents of this article are as follows:1.This article first introduces some common binary classification algorithms and anomaly detection algorithms.Then analyze the requirements of elevator safety monitoring in combination with the application scenarios of modern elevator equipment,and briefly explain the functional and non-functional requirements of elevator safety monitoring.Finally,it shows the overall design of the elevator safety monitoring technology and implementation,as well as the main content and functions of each part.2.To realize the real-time floor judgment of elevator equipment,this part analyzes the acceleration vibration signal and altitude signal in time domain.After analyzing and processing the data and several experiments,we find the connection and law between the acceleration signal and the operation of elevator equipment.Through the improved kmeans clustering algorithm,the floor distribution model of the elevator equipment is obtained,and then the real-time floor of the elevator equipment is finally calculated according to the relationship between the elevator equipment running time and the floor changes.Each elevator equipment is not necessarily the same,so the initialization algorithm is designed so that the real-time floor judgment algorithm can be applied to different elevator equipment.The obtained real-time floor can provide guarantee for locating abnormal floors and calculate and obtain other relevant elevator equipment operating parameters.3.Realize the elevator spectrum abnormality detection based on the stack type autoencoder.This part analyzes the high frequency acceleration and vibration signal from the frequency domain angle.Firstly,a type of support vector machine is used to detect the abnormality of the vibration acceleration spectrum of the elevator,and then a method of stacking self-encoder is introduced to detect the abnormality of the vibration acceleration spectrum,and the two are compared and analyzed.4.Combined with the real-time floor judgment algorithm of the elevator equipment and the abnormal detection algorithm of the vibration acceleration spectrum of the elevator equipment,the abnormal monitoring technology of the elevator equipment is finally implemented.And through the data experiment test and field test,completed the research and implementation of elevator safety monitoring technology in this paper.